
import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
    import torch


__lowerCAmelCase     :	Any       			=  random.Random()

def a__       (			A_,	A_=1.0,	A_=None,	A_=None   ):






  '''simple docstring'''





  if rng is None:
    __magic_name__								=			global_rng

  __magic_name__								=			[]
  for batch_idx in range(shape[0]   ):
    values.append([]   )
    for _ in range(shape[1]   ):
      values[-1].append(rng.random() * scale   )

  return values




@require_torch
class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					Any=7     ,				UpperCamelCase__     :					List[str]=400     ,				UpperCamelCase__     :					Tuple=2000     ,				UpperCamelCase__     :					List[str]=1     ,				UpperCamelCase__     :					Optional[Any]=0.0     ,				UpperCamelCase__     :					Union[str, Any]=1_6000     ,				UpperCamelCase__     :					Tuple=True     ,				UpperCamelCase__     :					Dict=80     ,				UpperCamelCase__     :					List[str]=16     ,				UpperCamelCase__     :					Union[str, Any]=64     ,				UpperCamelCase__     :					List[Any]="hann_window"     ,				UpperCamelCase__     :					Dict=80     ,				UpperCamelCase__     :					Any=7600     ,				UpperCamelCase__     :					Any=1E-10     ,				UpperCamelCase__     :					List[Any]=True     ,				)     ->      Any:







    """simple docstring"""






    __magic_name__								=			parent
    __magic_name__								=			batch_size
    __magic_name__								=			min_seq_length
    __magic_name__								=			max_seq_length
    __magic_name__								=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
    __magic_name__								=			feature_size
    __magic_name__								=			padding_value
    __magic_name__								=			sampling_rate
    __magic_name__								=			do_normalize
    __magic_name__								=			num_mel_bins
    __magic_name__								=			hop_length
    __magic_name__								=			win_length
    __magic_name__								=			win_function
    __magic_name__								=			fmin
    __magic_name__								=			fmax
    __magic_name__								=			mel_floor
    __magic_name__								=			return_attention_mask
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      List[str]:







    """simple docstring"""






    return {
        "feature_size": self.feature_size,
        "padding_value": self.padding_value,
        "sampling_rate": self.sampling_rate,
        "do_normalize": self.do_normalize,
        "num_mel_bins": self.num_mel_bins,
        "hop_length": self.hop_length,
        "win_length": self.win_length,
        "win_function": self.win_function,
        "fmin": self.fmin,
        "fmax": self.fmax,
        "mel_floor": self.mel_floor,
        "return_attention_mask": self.return_attention_mask,
    }
  def 				_lowercase				(					self     :					Tuple     ,				UpperCamelCase__     :					Optional[int]=False     ,				UpperCamelCase__     :					Union[str, Any]=False       )     ->      Union[str, Any]:







    """simple docstring"""






    def _flatten(UpperCamelCase__     :					Optional[Any]       ):
      return list(itertools.chain(*UpperCamelCase__       )       )

    if equal_length:
      __magic_name__								=			floats_list((self.batch_size, self.max_seq_length)       )
    else:
      # make sure that inputs increase in size
      __magic_name__								=			[
          _flatten(floats_list((x, self.feature_size)       )       )
          for x in range(self.min_seq_length     ,				self.max_seq_length     ,				self.seq_length_diff       )
      ]

    if numpify:
      __magic_name__								=			[np.asarray(UpperCamelCase__       ) for x in speech_inputs]

    return speech_inputs





  def 				_lowercase				(					self     :					Optional[Any]     ,				UpperCamelCase__     :					Union[str, Any]=False     ,				UpperCamelCase__     :					Union[str, Any]=False       )     ->      Optional[int]:







    """simple docstring"""






    if equal_length:
      __magic_name__								=			[floats_list((self.max_seq_length, self.num_mel_bins)       ) for _ in range(self.batch_size       )]
    else:
      # make sure that inputs increase in size
      __magic_name__								=			[
          floats_list((x, self.num_mel_bins)       )
          for x in range(self.min_seq_length     ,				self.max_seq_length     ,				self.seq_length_diff       )
      ]

    if numpify:
      __magic_name__								=			[np.asarray(UpperCamelCase__       ) for x in speech_inputs]

    return speech_inputs







@require_torch
class 			UpperCAmelCase_     (					_A    ,			unittest.TestCase	):


  '''simple docstring'''







  a__       			=						SpeechTaFeatureExtractor
  def 				_lowercase				(					self     :					Dict       )     ->      int:







    """simple docstring"""






    __magic_name__								=			SpeechTaFeatureExtractionTester(self       )
  def 				_lowercase				(					self     :					str     ,				UpperCamelCase__     :					List[Any]       )     ->      Tuple:







    """simple docstring"""






    self.assertTrue(np.all(np.mean(UpperCamelCase__     ,				axis=0       ) < 1E-3       )       )
    self.assertTrue(np.all(np.abs(np.var(UpperCamelCase__     ,				axis=0       ) - 1       ) < 1E-3       )       )
  def 				_lowercase				(					self     :					str       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
    # create three inputs of length 800, 1000, and 1200
    __magic_name__								=			[floats_list((1, x)       )[0] for x in range(800     ,				1400     ,				200       )]
    __magic_name__								=			[np.asarray(UpperCamelCase__       ) for speech_input in speech_inputs]

    # Test not batched input
    __magic_name__								=			feat_extract(speech_inputs[0]     ,				return_tensors="""np"""       ).input_values
    __magic_name__								=			feat_extract(np_speech_inputs[0]     ,				return_tensors="""np"""       ).input_values
    self.assertTrue(np.allclose(UpperCamelCase__     ,				UpperCamelCase__     ,				atol=1E-3       )       )

    # Test batched
    __magic_name__								=			feat_extract(UpperCamelCase__     ,				return_tensors="""np"""       ).input_values
    __magic_name__								=			feat_extract(UpperCamelCase__     ,				return_tensors="""np"""       ).input_values
    for enc_seq_a, enc_seq_a in zip(UpperCamelCase__     ,				UpperCamelCase__       ):
      self.assertTrue(np.allclose(UpperCamelCase__     ,				UpperCamelCase__     ,				atol=1E-3       )       )
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
    __magic_name__								=			[floats_list((1, x)       )[0] for x in range(800     ,				1400     ,				200       )]

    __magic_name__								=			["""longest""", """max_length""", """do_not_pad"""]
    __magic_name__								=			[None, 1600, None]
    for max_length, padding in zip(UpperCamelCase__     ,				UpperCamelCase__       ):
      __magic_name__								=			feat_extract(UpperCamelCase__     ,				padding=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				return_tensors="""np"""       )
      __magic_name__								=			processed.input_values

      self._check_zero_mean_unit_variance(input_values[0][:800]       )
      self.assertTrue(input_values[0][800:].sum() < 1E-6       )
      self._check_zero_mean_unit_variance(input_values[1][:1000]       )
      self.assertTrue(input_values[0][1000:].sum() < 1E-6       )
      self._check_zero_mean_unit_variance(input_values[2][:1200]       )
  def 				_lowercase				(					self     :					Dict       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
    __magic_name__								=			range(800     ,				1400     ,				200       )
    __magic_name__								=			[floats_list((1, x)       )[0] for x in lengths]

    __magic_name__								=			["""longest""", """max_length""", """do_not_pad"""]
    __magic_name__								=			[None, 1600, None]

    for max_length, padding in zip(UpperCamelCase__     ,				UpperCamelCase__       ):
      __magic_name__								=			feat_extract(UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				padding=UpperCamelCase__       )
      __magic_name__								=			processed.input_values

      self._check_zero_mean_unit_variance(input_values[0][:800]       )
      self._check_zero_mean_unit_variance(input_values[1][:1000]       )
      self._check_zero_mean_unit_variance(input_values[2][:1200]       )
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
    __magic_name__								=			[floats_list((1, x)       )[0] for x in range(800     ,				1400     ,				200       )]
    __magic_name__								=			feat_extract(
        UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=1000     ,				padding="""max_length"""     ,				return_tensors="""np"""       )
    __magic_name__								=			processed.input_values

    self._check_zero_mean_unit_variance(input_values[0, :800]       )
    self._check_zero_mean_unit_variance(input_values[1]       )
    self._check_zero_mean_unit_variance(input_values[2]       )
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
    __magic_name__								=			[floats_list((1, x)       )[0] for x in range(800     ,				1400     ,				200       )]
    __magic_name__								=			feat_extract(
        UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=1000     ,				padding="""longest"""     ,				return_tensors="""np"""       )
    __magic_name__								=			processed.input_values

    self._check_zero_mean_unit_variance(input_values[0, :800]       )
    self._check_zero_mean_unit_variance(input_values[1, :1000]       )
    self._check_zero_mean_unit_variance(input_values[2]       )

    # make sure that if max_length < longest -> then pad to max_length
    self.assertTrue(input_values.shape == (3, 1000)       )

    __magic_name__								=			[floats_list((1, x)       )[0] for x in range(800     ,				1400     ,				200       )]
    __magic_name__								=			feat_extract(
        UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=2000     ,				padding="""longest"""     ,				return_tensors="""np"""       )
    __magic_name__								=			processed.input_values

    self._check_zero_mean_unit_variance(input_values[0, :800]       )
    self._check_zero_mean_unit_variance(input_values[1, :1000]       )
    self._check_zero_mean_unit_variance(input_values[2]       )

    # make sure that if max_length > longest -> then pad to longest
    self.assertTrue(input_values.shape == (3, 1200)       )
  def 				_lowercase				(					self     :					int       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
    __magic_name__								=			np.random.rand(100       ).astype(np.floataa       )
    __magic_name__								=			np_speech_inputs.tolist()

    for inputs in [py_speech_inputs, np_speech_inputs]:
      __magic_name__								=			feature_extractor.pad([{"""input_values""": inputs}]     ,				return_tensors="""np"""       )
      self.assertTrue(np_processed.input_values.dtype == np.floataa       )
      __magic_name__								=			feature_extractor.pad([{"""input_values""": inputs}]     ,				return_tensors="""pt"""       )
      self.assertTrue(pt_processed.input_values.dtype == torch.floataa       )
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
    # create three inputs of length 800, 1000, and 1200
    __magic_name__								=			[floats_list((1, x)       )[0] for x in range(800     ,				1400     ,				200       )]
    __magic_name__								=			[np.asarray(UpperCamelCase__       ) for speech_input in speech_inputs]

    # Test feature size
    __magic_name__								=			feature_extractor(audio_target=UpperCamelCase__     ,				padding=UpperCamelCase__     ,				return_tensors="""np"""       ).input_values
    self.assertTrue(input_values.ndim == 3       )
    self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins       )

    # Test not batched input
    __magic_name__								=			feature_extractor(speech_inputs[0]     ,				return_tensors="""np"""       ).input_values
    __magic_name__								=			feature_extractor(np_speech_inputs[0]     ,				return_tensors="""np"""       ).input_values
    self.assertTrue(np.allclose(UpperCamelCase__     ,				UpperCamelCase__     ,				atol=1E-3       )       )

    # Test batched
    __magic_name__								=			feature_extractor(UpperCamelCase__     ,				return_tensors="""np"""       ).input_values
    __magic_name__								=			feature_extractor(UpperCamelCase__     ,				return_tensors="""np"""       ).input_values
    for enc_seq_a, enc_seq_a in zip(UpperCamelCase__     ,				UpperCamelCase__       ):
      self.assertTrue(np.allclose(UpperCamelCase__     ,				UpperCamelCase__     ,				atol=1E-3       )       )

    # Test 2-D numpy arrays are batched.
    __magic_name__								=			[floats_list((1, x)       )[0] for x in (800, 800, 800)]
    __magic_name__								=			np.asarray(UpperCamelCase__       )
    __magic_name__								=			feature_extractor(UpperCamelCase__     ,				return_tensors="""np"""       ).input_values
    __magic_name__								=			feature_extractor(UpperCamelCase__     ,				return_tensors="""np"""       ).input_values
    for enc_seq_a, enc_seq_a in zip(UpperCamelCase__     ,				UpperCamelCase__       ):
      self.assertTrue(np.allclose(UpperCamelCase__     ,				UpperCamelCase__     ,				atol=1E-3       )       )
  def 				_lowercase				(					self     :					List[str]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			self.feat_extract_tester.prepare_inputs_for_target()
    __magic_name__								=			self.feature_extraction_class(**self.feat_extract_dict       )
    __magic_name__								=			feat_extract.model_input_names[0]

    __magic_name__								=			BatchFeature({input_name: speech_inputs}       )

    self.assertTrue(all(len(UpperCamelCase__       ) == len(UpperCamelCase__       ) for x, y in zip(UpperCamelCase__     ,				processed_features[input_name]       )       )       )

    __magic_name__								=			self.feat_extract_tester.prepare_inputs_for_target(equal_length=UpperCamelCase__       )
    __magic_name__								=			BatchFeature({input_name: speech_inputs}     ,				tensor_type="""np"""       )

    __magic_name__								=			processed_features[input_name]

    if len(batch_features_input.shape       ) < 3:
      __magic_name__								=			batch_features_input[:, :, None]

    self.assertTrue(
        batch_features_input.shape
        == (self.feat_extract_tester.batch_size, len(speech_inputs[0]       ), self.feat_extract_tester.num_mel_bins)       )
  @require_torch
  def 				_lowercase				(					self     :					List[str]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			self.feat_extract_tester.prepare_inputs_for_target(equal_length=UpperCamelCase__       )
    __magic_name__								=			self.feature_extraction_class(**self.feat_extract_dict       )
    __magic_name__								=			feat_extract.model_input_names[0]

    __magic_name__								=			BatchFeature({input_name: speech_inputs}     ,				tensor_type="""pt"""       )

    __magic_name__								=			processed_features[input_name]

    if len(batch_features_input.shape       ) < 3:
      __magic_name__								=			batch_features_input[:, :, None]

    self.assertTrue(
        batch_features_input.shape
        == (self.feat_extract_tester.batch_size, len(speech_inputs[0]       ), self.feat_extract_tester.num_mel_bins)       )
  @require_torch
  def 				_lowercase				(					self     :					Tuple       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.feature_extraction_class(**self.feat_extract_dict       )
    __magic_name__								=			self.feat_extract_tester.prepare_inputs_for_target()
    __magic_name__								=			feat_extract.model_input_names[0]

    __magic_name__								=			BatchFeature({input_name: speech_inputs}       )

    __magic_name__								=			feat_extract.num_mel_bins  # hack!

    __magic_name__								=			feat_extract.pad(UpperCamelCase__     ,				padding="""longest"""     ,				return_tensors="""np"""       )[input_name]
    __magic_name__								=			feat_extract.pad(UpperCamelCase__     ,				padding="""longest"""     ,				return_tensors="""pt"""       )[input_name]

    self.assertTrue(abs(input_np.astype(np.floataa       ).sum() - input_pt.numpy().astype(np.floataa       ).sum()       ) < 1E-2       )
  def 				_lowercase				(					self     :					str       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.feat_extract_dict
    __magic_name__								=			True
    __magic_name__								=			self.feature_extraction_class(**UpperCamelCase__       )
    __magic_name__								=			self.feat_extract_tester.prepare_inputs_for_target()
    __magic_name__								=			[len(UpperCamelCase__       ) for x in speech_inputs]
    __magic_name__								=			feat_extract.model_input_names[0]

    __magic_name__								=			BatchFeature({input_name: speech_inputs}       )

    __magic_name__								=			feat_extract.num_mel_bins  # hack!

    __magic_name__								=			feat_extract.pad(UpperCamelCase__     ,				padding="""longest"""     ,				return_tensors="""np"""       )
    self.assertIn("""attention_mask"""     ,				UpperCamelCase__       )
    self.assertListEqual(list(processed.attention_mask.shape       )     ,				list(processed[input_name].shape[:2]       )       )
    self.assertListEqual(processed.attention_mask.sum(-1       ).tolist()     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					int       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			self.feat_extract_dict
    __magic_name__								=			True
    __magic_name__								=			self.feature_extraction_class(**UpperCamelCase__       )
    __magic_name__								=			self.feat_extract_tester.prepare_inputs_for_target()
    __magic_name__								=			[len(UpperCamelCase__       ) for x in speech_inputs]
    __magic_name__								=			feat_extract.model_input_names[0]

    __magic_name__								=			BatchFeature({input_name: speech_inputs}       )
    __magic_name__								=			min(UpperCamelCase__       )

    __magic_name__								=			feat_extract.num_mel_bins  # hack!

    __magic_name__								=			feat_extract.pad(
        UpperCamelCase__     ,				padding="""max_length"""     ,				max_length=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				return_tensors="""np"""       )
    self.assertIn("""attention_mask"""     ,				UpperCamelCase__       )
    self.assertListEqual(
        list(processed_pad.attention_mask.shape       )     ,				[processed_pad[input_name].shape[0], max_length]       )
    self.assertListEqual(
        processed_pad.attention_mask[:, :max_length].sum(-1       ).tolist()     ,				[max_length for x in speech_inputs]       )
  def 				_lowercase				(					self     :					Any     ,				UpperCamelCase__     :					Tuple       )     ->      Tuple:







    """simple docstring"""






    from datasets import load_dataset

    __magic_name__								=			load_dataset("""hf-internal-testing/librispeech_asr_dummy"""     ,				"""clean"""     ,				split="""validation"""       )
    # automatic decoding with librispeech
    __magic_name__								=			ds.sort("""id"""       ).select(range(UpperCamelCase__       )       )[:num_samples]["""audio"""]

    return [x["array"] for x in speech_samples]
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			torch.tensor(
        [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
         3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
         2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
         4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
         7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
         4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]       )
    # fmt: on

    __magic_name__								=			self._load_datasamples(1       )
    __magic_name__								=			SpeechTaFeatureExtractor()
    __magic_name__								=			feature_extractor(UpperCamelCase__     ,				return_tensors="""pt"""       ).input_values
    self.assertEquals(input_values.shape     ,				(1, 9_3680)       )
    self.assertTrue(torch.allclose(input_values[0, :30]     ,				UpperCamelCase__     ,				atol=1E-6       )       )





  def 				_lowercase				(					self     :					Tuple       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			torch.tensor(
        [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
         -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
         -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
         -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]       )
    # fmt: on

    __magic_name__								=			self._load_datasamples(1       )
    __magic_name__								=			SpeechTaFeatureExtractor()
    __magic_name__								=			feature_extractor(audio_target=UpperCamelCase__     ,				return_tensors="""pt"""       ).input_values
    self.assertEquals(input_values.shape     ,				(1, 366, 80)       )
    self.assertTrue(torch.allclose(input_values[0, 0, :30]     ,				UpperCamelCase__     ,				atol=1E-4       )       )



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


__lowerCAmelCase     :	Optional[Any]       			=  'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			_ask_options(
      """In which compute environment are you running?""",	["""This machine""", """AWS (Amazon SageMaker)"""],	_convert_compute_environment,	)
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    __magic_name__								=			get_sagemaker_input()
  else:
    __magic_name__								=			get_cluster_input()
  return config

def a__       (			A_=None   ):






  '''simple docstring'''





  if subparsers is not None:
    __magic_name__								=			subparsers.add_parser("""config""",	description=A_   )
  else:
    __magic_name__								=			argparse.ArgumentParser("""Accelerate config command""",	description=A_   )

  parser.add_argument(
      """--config_file""",	default=A_,	help=(
          """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
          """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
          """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
          """with 'huggingface'."""
      ),	)

  if subparsers is not None:
    parser.set_defaults(func=A_   )
  return parser

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			get_user_input()
  if args.config_file is not None:
    __magic_name__								=			args.config_file
  else:
    if not os.path.isdir(A_   ):
      os.makedirs(A_   )
    __magic_name__								=			default_yaml_config_file

  if config_file.endswith(""".json"""   ):
    config.to_json_file(A_   )
  else:
    config.to_yaml_file(A_   )
  print(f'''accelerate configuration saved at {config_file}'''   )

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			config_command_parser()
  __magic_name__								=			parser.parse_args()
  config_command(A_   )


if __name__ == "__main__":
    main()

